Project Details
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Knee MRI with in situ mechanical loading using prospective motion correction

Subject Area Orthopaedics, Traumatology, Reconstructive Surgery
Medical Physics, Biomedical Technology
Term from 2017 to 2022
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 320713197
 
Osteoarthritis of the knee joint is a major cause of morbidity and often originates from disturbed joint biomechanics. Magnetic resonance imaging (MRI) has been established as a gold standard for the detection of the associated degenerative changes (meniscus and cartilage defects, synovialitis). However, MRI cannot directly detect the underlying disturbed biomechanics, e.g. in pathologies such as patella instabilities and maltracking. To this end, measurements under mechanical loading are required, which are usually hampered by involuntary subject motion. To date, the associated image artefacts cannot be prevented or corrected with conventional MRI methods. In a pilot project we could show that such load-induced motion artefacts can be corrected with prospective motion correction during the MRI experiment. For the envisaged project, MRI methods for the investigation of the patellofemoral kinematics and biomechanics under joint flexion and mechanical loading will be developed and validated. Load-induced cartilage compression and associated changes of MR-specific relaxation parameters (T2, T1rho) are to be determined. To this end, the implementation of prospective motion correction into the MRI sequence code is required, but also the development of a postprocessing pipeline for visualisation and quantitative assessment of the acquired data sets (segmentation, registration, assessment of cartilage thickness, contact areas, etc.). The developed methods will be validated in a study on healthy subjects and patients with patella instability. Clinical markers for the diagnosis of this pathology are to be established and the results of medial patellofemoral ligament surgery are to be evaluated in an interventional study.
DFG Programme Research Grants
Co-Investigator Professor Dr. Maxim Zaitsev
 
 

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